The System Design and Implementation of a Two-Wheeled Self-Balancing and Motion Control

随着社会的发展和科技的进步，移动机器人的应用领域愈发广泛，机器人面临的工作环境和任务需求也变得复杂多样，人们对它的要求和期待也越来越高。两轮自平衡机器人以其结构简单、运动灵活、占地空间小等优点受到了人们的重视，成为移动机器人研究领域的一个重要分支。 本文设计了一台重心位于轮轴下方的非载人两轮自平衡机器人基础运动平台。采用两轮同轴独立驱动，在运动过程中能较好地保持平衡。同时，在该运动平台上，设计实现具有一定载荷能力的稳定平台，用来搭载稳定性要求更高的设备。 本文的主要工作如下： 首先，以两轮机器人系统为研究对象，采用牛顿力学法建立系统动力学模型，将模型进行线性化处理，同时实现解耦，并对解耦...With the development of the society and the progress of science and technology, the mobile robots have been in various field, but the working environment and mission requirements also become complicated, which determines the classification of robots has been increasingly refined to meet different needs. The requirements and expectations of the people on the robots are also getting higher and highe...学位：工程硕士院系专业：航空航天学院_工程硕士(控制工程)学号：2322013115337

Optimization and Evaluation of a Proportional Derivative Controller for Planar Arm Movement

In most clinical applications of functional electrical stimulation (FES), the timing and amplitude of electrical stimuli have been controlled by open-loop pattern generators. The control of upper extremity reaching movements, however, will require feedback control to achieve the required precision. Here we present three controllers using proportional derivative (PD) feedback to stimulate six arm muscles, using two joint angle sensors. Controllers were first optimized and then evaluated on a computational arm model that includes musculoskeletal dynamics. Feedback gains were optimized by minimizing a weighted sum of position errors and muscle forces. Generalizability of the controllers was evaluated by performing movements for which the controller was not optimized, and robustness was tested via model simulations with randomly weakened muscles. Robustness was further evaluated by adding joint friction and doubling the arm mass. After optimization with a properly weighted cost function, all PD controllers performed fast, accurate, and robust reaching movements in simulation. Oscillatory behavior was seen after improper tuning. Performance improved slightly as the complexity of the feedback gain matrix increased

Параметричний синтез комбінованих автоматичних систем регулювання з цифровими під-регуляторами

The problem of calculation of combined digital automatic regulation systems was considered. The main purpose of the study was to develop, on the basis of the principle of invariance, a method of parametric synthesis of combined systems with digital PID-controllers. To calculate the closed loop of the combined system taking into account the requirements to its stability and quality of regulation, the method of multicriterial parametric optimization was used. Conditions of physical realization of the ideal compensator on the basis of absence of a link with a negative net delay and ideal differentiating links in its transfer function were indicated. Choice of the structure of the transfer function of the real compensator was substantiated on the basis of coincidence of its complex frequency response with the complex frequency response of the ideal compensator at zero and operating frequencies. The results of synthesis of a combined system with a digital PID-controller and a real object have shown that the proposed method ensures quality of the digital system practically equivalent to that of the continuous system which is ensured by the choice of the structure of the discrete algorithm of the closed loop operation and the corresponding choice of the period of discreteness. In particular, it has been found that the use of the compensator enables an approximately fivefold reduction of the maximum dynamic deviation of the regulated value and approximately threefold improvement of the system dynamic precision according to integral absolute quality estimates. The developed approach can be applied for synthesis of combined systems with digital PID-controllers and a wide class of objectsПроведен анализ особенностей реализации комбинированных систем в зависимости от способа включения компенсатора. Предложен способ параметрического синтеза цифровых комбинированных автоматических систем регулирования на основе принципа инвариантности с расчетом замкнутого контура методом многокритериальной параметрической оптимизации. По разработанному подходу приведен пример синтеза комбинированной системы с цифровым ПИД-регулятором и реальным объектомПроведено аналіз особливостей реалізації комбінованих систем в залежності від способу вмикання компенсатора. Запропоновано спосіб параметричного синтезу цифрових комбінованих автоматичних систем регулювання на основі принципу інваріантності з розрахунком замкненого контуру за методом багатокритеріальної параметричної оптимізації. За розробленим підходом наведено приклад синтезу комбінованої системи з цифровим ПІД-регулятором та реальним об’єкто

An optimized proportional-derivative controller for the human upper extremity with gravity

When Functional Electrical Stimulation (FES) is used to restore movement in subjects with spinal cord injury (SCI), muscle stimulation patterns should be selected to generate accurate and efficient movements. Ideally, the controller for such a neuroprosthesis will have the simplest architecture possible, to facilitate translation into a clinical setting. In this study, we used the simulated annealing algorithm to optimize two proportional-derivative (PD) feedback controller gain sets for a 3-dimensional arm model that includes musculoskeletal dynamics and has 5 degrees of freedom and 22 muscles, performing goal-oriented reaching movements. Controller gains were optimized by minimizing a weighted sum of position errors, orientation errors, and muscle activations. After optimization, gain performance was evaluated on the basis of accuracy and efficiency of reaching movements, along with three other benchmark gain sets not optimized for our system, on a large set of dynamic reaching movements for which the controllers had not been optimized, to test ability to generalize. Robustness in the presence of weakened muscles was also tested. The two optimized gain sets were found to have very similar performance to each other on all metrics, and to exhibit significantly better accuracy, compared with the three standard gain sets. All gain sets investigated used physiologically acceptable amounts of muscular activation. It was concluded that optimization can yield significant improvements in controller performance while still maintaining muscular efficiency, and that optimization should be considered as a strategy for future neuroprosthesis controller design

On the Relationship Between Muscle Synergies and Redundant Degrees of Freedom in Musculoskeletal Systems

It has been suggested that the human nervous system controls motions in the task (or operational) space. However, little attention has been given to the separation of the control of the task-related and task-irrelevant degrees of freedom.Aim: We investigate how muscle synergies may be used to separately control the task-related and redundant degrees of freedom in a computational model.Approach: We generalize an existing motor control model, and assume that the task and redundant spaces have orthogonal basis vectors. This assumption originates from observations that the human nervous system tightly controls the task-related variables, and leaves the rest uncontrolled. In other words, controlling the variables in one space does not affect the other space; thus, the actuations must be orthogonal in the two spaces. We implemented this assumption in the model by selecting muscle synergies that produce force vectors with orthogonal directions in the task and redundant spaces.Findings: Our experimental results show that the orthogonality assumption performs well in reconstructing the muscle activities from the measured kinematics/dynamics in the task and redundant spaces. Specifically, we found that approximately 70% of the variation in the measured muscle activity can be captured with the orthogonality assumption, while allowing efficient separation of the control in the two spaces.Implications: The developed motor control model is a viable tool in real-time simulations of musculoskeletal systems, as well as model-based control of bio-mechatronic systems, where a computationally efficient representation of the human motion controller is needed

Achieving Practical Functional Electrical Stimulation-driven Reaching Motions In An Individual With Tetraplegia

Functional electrical stimulation (FES) is a promising technique for restoring the ability to complete reaching motions to individuals with tetraplegia due to a spinal cord injury (SCI). FES has proven to be a successful technique for controlling many functional tasks such as grasping, standing, and even limited walking. However, translating these successes to reaching motions has proven difficult due to the complexity of the arm and the goaldirected nature of reaching motions. The state-of-the-art systems either use robots to assist the FES-driven reaching motions or control the arm of healthy subjects to complete planar motions. These controllers do not directly translate to controlling the full-arm of an individual with tetraplegia because the muscle capabilities of individuals with spinal cord injuries are unique and often limited due to muscle atrophy and the loss of function caused by lower motor neuron damage. This dissertation aims to develop a full-arm FES-driven reaching controller that is capable of achieving 3D reaching motions in an individual with a spinal cord injury. Aim 1 was to develop a complete-arm FES-driven reaching controller that can hold static hand positions for an individual with high tetraplegia due to SCI. We developed a combined feedforward-feedback controller which used the subject-specific model to automatically determine the muscle stimulation commands necessary to hold a desired static hand position. Aim 2 was to develop a subject-specific model-based control strategy to use FES to drive the arm of an individual with high tetraplegia due to SCI along a desired path in the subject’s workspace. We used trajectory optimization to find feasible trajectories which explicitly account for the unique muscle characteristics and the simulated arm dynamics of our subject with tetraplegia. We then developed a model predictive control controller to iii control the arm along the desired trajectory. The controller developed in this dissertation is a significant step towards restoring full arm reaching function to individuals with spinal cord injuries

Combined feedforward and feedback control of a redundant, nonlinear, dynamic musculoskeletal system

A functional electrical stimulation controller is presented that uses a combination of feedforward and feedback for arm control in high-level injury. The feedforward controller generates the muscle activations nominally required for desired movements, and the feedback controller corrects for errors caused by muscle fatigue and external disturbances. The feedforward controller is an artificial neural network (ANN) which approximates the inverse dynamics of the arm. The feedback loop includes a PID controller in series with a second ANN representing the nonlinear properties and biomechanical interactions of muscles and joints. The controller was designed and tested using a two-joint musculoskeletal model of the arm that includes four mono-articular and two bi-articular muscles. Its performance during goal-oriented movements of varying amplitudes and durations showed a tracking error of less than 4° in ideal conditions, and less than 10° even in the case of considerable fatigue and external disturbances